Search

The authors present a scientometric procedure comparing engineering education (EE) publication output in different countries. Selected European countries were compared using a snapshot of authors published in EE journals during a two-year period – 895 in all. The entire publication careers of these authors – 39,322 publications – were analysed to determine the breakdown of educational and non-educational publications. Spain and the UK produced the most publications, and we propose explanations based on relevant national policies. France, Germany, and Italy had notably fewer EE publications relative to their general engineering and science research outputs, whereas Portugal, Ireland, and three Nordic countries were the opposite. Countries varied widely in the ratio of educational to non-educational publications. Non-educational publications typically had a greater impact on authors' h-index values. We believe this procedure can longitudinally map and compare EE publication output in and across Europe, providing a valuable resource for policy-makers and researchers in the region. [ABSTRACT FROM AUTHOR]

Photoexcited molecular trajectories on potential energy surfaces (PESs) prior to thermalization are intimately connected to the photochemical reaction outcome. The excited‐state trajectories of a diplatinum complex featuring photo‐activated metal–metal σ‐bond formation and associated Pt−Pt stretching motions were detected in real time using femtosecond wide‐angle X‐ray solution scattering. The observed motions correspond well with coherent vibrational wavepacket motions detected by femtosecond optical transient absorption. Two key coordinates for intersystem crossing have been identified, the Pt−Pt bond length and the orientation of the ligands coordinated with the platinum centers, along which the excited‐state trajectories can be projected onto the calculated PESs of the excited states. This investigation has gleaned novel insight into electronic transitions occurring on the time scales of vibrational motions measured in real time, revealing ultrafast nonadiabatic or non‐equilibrium processes along excited‐state trajectories involving multiple excited‐state PESs. [ABSTRACT FROM AUTHOR]

Photoexcited molecular trajectories on potential energy surfaces (PESs) prior to thermalization are intimately connected to the photochemical reaction outcome. The excited‐state trajectories of a diplatinum complex featuring photo‐activated metal–metal σ‐bond formation and associated Pt−Pt stretching motions were detected in real time using femtosecond wide‐angle X‐ray solution scattering. The observed motions correspond well with coherent vibrational wavepacket motions detected by femtosecond optical transient absorption. Two key coordinates for intersystem crossing have been identified, the Pt−Pt bond length and the orientation of the ligands coordinated with the platinum centers, along which the excited‐state trajectories can be projected onto the calculated PESs of the excited states. This investigation has gleaned novel insight into electronic transitions occurring on the time scales of vibrational motions measured in real time, revealing ultrafast nonadiabatic or non‐equilibrium processes along excited‐state trajectories involving multiple excited‐state PESs. [ABSTRACT FROM AUTHOR]

Bimetallic platinum complexes have interesting luminescent properties and feature long-lasting vibrational coherence and ultrafast intersystem crossing (ISC) after photoexcitation. Ultrafast triplet formation is driven by very strong spin-orbit coupling in these platinum (II) systems, where relativistic theoretical approaches beyond first-order perturbation theory are desirable. Using a fully variational relativistic theoretical method recently developed by the authors, we investigate the origins of ultrafast ISC in the [Pt(ppy) (μ-tBu2pz)]2 complex (ppy = phenylpyridine, pz = pyrazolate). Spin-orbit coupling values, evaluated along a Born-Oppenheimer molecular dynamics trajectory, are used to propagate electronic populations in time. Using this technique, we estimate ultrafast ISC rates of 15–134 fs in this species for the possible ISC pathways into the three low-lying triplet states. [ABSTRACT FROM AUTHOR]

The change in electronic state from one spin multiplicity to another, known as intersystem crossing, occurs in molecules via the relativistic phenomenon of spin-orbit coupling. Current means of estimating intersystem crossing rates rely on the perturbative evaluation of spin-orbit coupling effects. This perturbative approach, valid in lighter atoms where spin-orbit coupling is weaker, is expected to break down for heavier elements where relativistic effects become dominant. Methods which incorporate spin-orbit effects variationally, such as the exact-two-component (X2C) method, will be necessary to treat this strong-coupling regime. We present a novel procedure which produces a diabatic basis of spin-pure electronic states coupled by spin-orbit terms, generated from fully variational relativistic calculations. This method is implemented within X2C using time-dependent density-functional theory and is compared to results from a perturbative relativistic study in the weak spin-orbit coupling regime. Additional calculations on a more strongly spin-orbit-coupled [ U O 2 C l 4 ] 2 − complex further illustrate the strengths of this method. This procedure will be valuable in the estimation of intersystem crossing rates within strongly spin-coupled species. [ABSTRACT FROM AUTHOR]

Two-coordinate transition metal complexes are exciting candidates for single-molecule magnets (SMMs) because their highly axial coordination environments lead to sizeable magnetic anisotropy. We report a series of five structurally related two-coordinate Fe(II) m-terphenyl complexes (4-R-2,6-Xyl2C6H2)2Fe [R = tBu (1), SiMe3 (2), H (3), Cl (4), CF3 (5)] where, by changing the functionalisation of the para-substituent (R), we alter their magnetic function. All five complexes are field-induced single-molecule magnets, with relaxation rates that are well-described by a combination of direct and Raman mechanisms. By using more electron donating R groups we were able to slow the rate of magnetic relaxation. Our ab initio calculations predict a large crystal field splitting (>850 cm−1) and sizeable zero-field splitting parameters (ca. −60 cm−1, ‖E‖ < 0.2 cm−1) for 1–5. These favourable magnetic properties suggest that m-terphenyl ligands have untapped potential as chemically versatile ligands able to impose highly axial crystal fields. [ABSTRACT FROM AUTHOR]

Monte Carlo methods are widespread in geophysics and have proved to be powerful in non-linear inverse problems. However, they are associated with significant practical challenges, including long calculation times, large output ensembles of Earth models, and difficulties in the appraisal of the results. This paper addresses some of these challenges using generative models, a family of tools that have recently attracted much attention in the machine learning literature. Generative models can, in principle, learn a probability distribution from a set of given samples and also provide a means for rapid generation of new samples which follow that approximated distribution. These two features make them well suited for application to the outputs of Monte Carlo algorithms. In particular, training a generative model on the posterior distribution of a Bayesian inference problem provides two main possibilities. First, the number of parameters in the generative model is much smaller than the number of values stored in the ensemble, leading to large compression rates. Secondly, once trained, the generative model can be used to draw any number of samples, thereby eliminating the dependence on an often large and unwieldy ensemble. These advantages pave new pathways for the use of Monte Carlo ensembles, including improved storage and communication of the results, enhanced calculation of numerical integrals, and the potential for convergence assessment of the Monte Carlo procedure. Here, these concepts are initially demonstrated using a simple synthetic example that scales into higher dimensions. They are then applied to a large ensemble of shear wave velocity models of the core–mantle boundary, recently produced in a Monte Carlo study. These examples demonstrate the effectiveness of using generative models to approximate posterior ensembles, and indicate directions to address various challenges in Monte Carlo inversion. [ABSTRACT FROM AUTHOR]

Benefits of industry engagement in engineering education for various stakeholders are well known, but there can be significant obstacles to initiating engagement. Using social capital theory, this study investigated how stakeholders initiate industry engagement in engineering education, and the main barriers from the stakeholders' perspectives. Semi-structured interviews were held with 21 professionals involved in the engineering industry (including academics and industry personnel). Inductive analysis showed industry engagement was initiated by all stakeholders, although reasons differed. Relationships between individual academics and their industry contacts were often key. Relying on these relationships was problematic when individuals changed roles or organisations. Relationships were also maintained through ongoing agreements or facilitated through third-party organisations. Key barriers included lack of invitations to industry stakeholders from universities, and unclear benefits to industry stakeholders of long-term engagement. The success of third parties in facilitating engagement may provide a framework for facilitating successful ongoing engagement. [ABSTRACT FROM AUTHOR]

Creativity is an important skill for engineers but many students face a lack of experience in idea generation, often compounded by low-self efficacy towards creativity. Providing students with online training modules has been suggested as one solution. However, the literature demonstrates using a computer can influence performance and self-efficacy in a different manner to using pen-and-paper. There is a current lack of research on whether computers may influence self-efficacy during idea generation tasks. Two experiments involving computer-based and pen-and-paper groups were designed. Groups were provided with templates that guided them through the process of applying an idea generation technique and were given sixteen minutes to generate ideas to solve a presented problem. Results revealed that students' performance was similar and using a computer does not negatively influence self-efficacy. This shows that students can engage in idea generation learning tasks using computer-based modes without negatively influencing performance or self-efficacy, suggesting an avenue for educators to engage students with building creativity skills. [ABSTRACT FROM AUTHOR]

Aluminosilicate glasses and melts are of paramount importance for geo- and materials sciences. They include most magmas, and are used to produce a wide variety of everyday materials, from windows to smartphone displays. Despite this importance, no general model exists with which to predict the atomic structure, thermodynamic and viscous properties of aluminosilicate melts. To address this, we introduce a deep learning framework, 'i-Melt', which combines a deep artificial neural network with thermodynamic equations. It is trained to predict 18 different latent and observed properties of melts and glasses in the K 2 O-Na 2 O-Al 2 O 3 -SiO 2 system, including configurational entropy, viscosity, optical refractive index, density, and Raman signals. Viscosity can be predicted in the 100–1015 log 10 Pa·s range using five different theoretical frameworks (Adam-Gibbs, Free Volume, MYEGA, VFT, Avramov-Milchev), with a precision equal to, or better than, 0.4 log 10 Pa·s on unseen data. Density and optical refractive index (through the Sellmeier equation) can be predicted with errors equal or lower than 0.02 and 0.006, respectively. Raman spectra for K 2 O-Na 2 O-Al 2 O 3 -SiO 2 glasses are also predicted, with a relatively high mean error of ∼25% due to the limited data set available for training. Latent variables can also be predicted with good precisions. For example, the glass transition temperature, T g , can be predicted to within 19 K, while the melt configurational entropy at the glass transition, Sconf(T g) , can be predicted to within 0.8 J mol−1 K−1. Applied to rhyolite compositions, i-Melt shows that the rheological threshold separating explosive and effusive eruptions correlates with an increase in the fraction of non-bridging oxygens in rhyolite melts as their alkali/Al ratio becomes larger than 1. Exploring further the effect of the K/(K + Na) ratio on the properties of alkali aluminosilicate melts with compositions varying along a simplified alkali magmatic series trend, we observe that K-rich melts have systematically different structures and higher viscosities compared to Na-rich melts. Combined with the effects of the K/(K + Na) ratio on other parameters, such as the solubility, solution mechanisms and speciation of volatile elements, this could ultimately influence the eruptive dynamics of volcanic systems emitting Na-rich or K-rich alkali magmas. [ABSTRACT FROM AUTHOR]

The effect of para-substitution upon the structural and electronic properties of a series of m-terphenyl lithium complexes [R–Ar#–Li]2 (R = t-Bu 1, SiMe32, H 3, Cl 4, CF35; where R–Ar# = 2,6-{2,6-Xyl}2-4-R-C6H2 and 2,6-Xyl = 2,6-Me2C6H3) has been investigated. X-ray crystallography reveals the complexes to be structurally similar, with little variation in C–M–C bond lengths and angles across the series. However, in-depth NMR spectroscopic studies reveal notable electronic differences, showing a linear correlation between the 7Li{1H} NMR chemical shifts of the para-substituted complexes and their Hammett constants. The flanking methyl protons exhibit a similar electronic shift in the 1H NMR spectra, which has been rationalised by the presence of through-space Li⋯H interactions, as evidenced by two-dimensional 7Li–1H heteronuclear Overhauser spectroscopy (HOESY). In both cases, electron-withdrawing substituents are found to cause an upfield peak shift. A computational analysis is employed to account for these trends. [ABSTRACT FROM AUTHOR]

Engineering professionals are expected to conduct their work in an ethical manner. However, students may receive limited instruction on ethics and demonstrate resistance to learning about ethics in practice, contributing to graduates being ill-prepared to face ethical dilemmas in the workplace. This study investigated the nascent learning by engineering students (N = 183) about the relevance of ethics to engineering practice in a two-hour workshop that included recounts of experiences that were of immediate relevance to engineering interns and graduates, through use of videos and a serious game. Thematic analysis of qualitative responses highlighted that students gained understanding of how ethics influences engineering practice in general, and how ethical issues influence the work of engineering professionals. Exposing students to the presence of ethical issues in practice through immediately relevant examples early during their studies is likely to improve the way they approach ethics throughout their studies, and subsequently, the workplace. [ABSTRACT FROM AUTHOR]

We develop a theoretical framework for framing and solving probabilistic linear(ized) inverse problems in function spaces. This is built on the statistical theory of Gaussian Processes, and allows results to be obtained independent of any basis, avoiding any difficulties associated with the fidelity of representation that can be achieved. We show that the results of Backus–Gilbert theory can be fully understood within our framework, although there is not an exact equivalence due to fundamental differences of philosophy between the two approaches. Nevertheless, our work can be seen to unify several strands of linear inverse theory, and connects it to a large body of work in machine learning. We illustrate the application of our theory using a simple example, involving determination of Earth's radial density structure. [ABSTRACT FROM AUTHOR]

By starting from a general framework for probabilistic continuous inversion (developed in Part I) and introducing discrete basis functions, we obtain the well-known algorithms for probabilistic least-squares inversion set out by Tarantola & Valette. In doing so, we establish a direct equivalence between the spatial covariance function that must be specified in continuous inversion, and the combination of basis functions and prior covariance matrix that must be chosen for discretized inversion. We show that the common choice of Tikhonov regularization (⁠|$\mathbf {C_m^{-1}} = \sigma ^2\mathbf {I}$|⁠) arises from a delta-function spatial covariance, and that this lies behind many of the artefacts commonly associated with discretized inversion. We show that other choices of spatial covariance function can be used to generate regularization matrices yielding substantially better results, and permitting localization of features even if global basis functions are used. We are also able to offer a straightforward explanation for the spectral leakage problem identified by Trampert & Snieder. [ABSTRACT FROM AUTHOR]

Contribution: This paper demonstrated on a large scale that explicit articulation of creativity-related learning goals on engineering syllabi is quite limited, and primarily limited to the first year of study. Engineering educators may need to do more to ensure creativity is explicitly addressed as an expected learning outcome within engineering syllabi. Background: Prior studies have shown inclusion of creativity within engineering syllabi and course activities are generally limited. Students may perceive educators do not value creativity, and their studies have limited influence on their creative skills. Studies conflict on whether creativity skills increase over completing an engineering degree. Creativity has been demonstrated to be an important skill for engineering professionals, but one not necessarily appropriately addressed in engineering programs. Few studies have attempted to quantify coverage of creativity material on a nation-wide scale, as opposed to in a single or select few institutions. Research Questions: 1) To what extent do engineering educators explicitly articulate creativity and innovation-related learning goals and material on their course syllabi? and 2) To what extent do engineering educators explicitly articulate exposure to, or instruction in the use of, creativity heuristics within their course syllabi? Methodology: The online publicly accessible course outlines of 1109 compulsory courses from 42 degree programs accredited by two national engineering accreditation bodies were qualitatively analyzed in a two-stage approach using document analysis. Findings: Approximately 2% of compulsory electrical engineering course outlines explicitly articulate creativity-related material; only one course articulated engaging students in using creativity heuristics. [ABSTRACT FROM AUTHOR]

Most linear inverse problems require regularization to ensure that robust and meaningful solutions can be found. Typically, Tikhonov-style regularization is used, whereby a preference is expressed for models that are somehow 'small' and/or 'smooth'. The strength of such preferences is expressed through one or more damping parameters, which control the character of the solution, and which must be set by the user. However, identifying appropriate values is often regarded as a matter of art, guided by various heuristics. As a result, such choices have often been the source of controversy and concern. By treating these as hyperparameters within a hierarchical Bayesian framework, we are able to obtain solutions that encompass the range of permissible regularization parameters. Furthermore, we show that these solutions are often well-approximated by those obtained via standard analysis using certain regularization choices which are—in a certain sense—optimal. We obtain algorithms for determining these optimal values in various cases of common interest, and show that they generate solutions with a number of attractive properties. A reference implementation of these algorithms, written in Python, accompanies this paper. [ABSTRACT FROM AUTHOR]

Problem-solving is a key engineering skill, yet is an area in which engineering graduates underperform. This paper investigates the potential of using web-based tools to teach students problem-solving techniques without the need to make use of class time. An idea generation experiment involving 90 students was designed. Students were surveyed about their study habits and reported they use electronic-based materials more than paper-based materials while studying, suggesting students may engage with web-based tools. Students then generated solutions to a problem task using either a paper-based template or an equivalent web interface. Students who used the web-based approach performed as well as students who used the paper-based approach, suggesting the technique can be successfully adopted and taught online. Web-based tools may therefore be adopted as supplementary material in a range of engineering courses as a way to increase students’ options for enhancing problem-solving skills. [ABSTRACT FROM PUBLISHER]

Efficient and accurate nuclear gradients are essential to performing molecular optimizations. Here for the first time we present analytical nuclear gradients for the parametric two-electron reduced-density-matrix method (p2-RDM), which uses the 2-RDM as the primary variable in calculations in lieu of the many-electron wavefunction. While numerical gradients require six energy evaluations for each atom, analytical gradients require only a single calculation for each geometry sampled. We present benchmark p2-RDM geometry optimizations that show analytical gradients reduce CPU times by as much as 80%, even for small molecules. We also use p2-RDM to evaluate the bond length alternation (BLA), or the difference in length between adjacent single and double bonds, of trans -polyacetylene (PA). We find that the BLA in the extrapolated limit to be 0.080 Å, in agreement with experiment and closely mirroring the prediction of the more expensive coupled-cluster with single and double excitations with perturbative triples (CCSD(T)). [ABSTRACT FROM AUTHOR]

Glacial isostatic adjustment (GIA) modeling is not only useful for understanding past relative sea‐level change but also for projecting future sea‐level change due to ongoing land deformation. However, GIA model predictions are subject to a range of uncertainties, most notably due to uncertainty in the input ice history. An effective way to reduce this uncertainty is to perform data‐model comparisons over a large ensemble of possible ice histories, but this is often impossible due to computational limitations. Here we address this problem by building a deep‐learning‐based GIA emulator that can mimic the behavior of a physics‐based GIA model while being computationally cheap to evaluate. Assuming a single 1‐D Earth rheology, our emulator shows 0.54 m mean absolute error on 150 out‐of‐sample testing data with <0.5 s emulation time. Using this emulator, two illustrative applications related to the calculation of barystatic sea level are provided for use by the sea‐level community. Plain Language Summary: Piecing together the history of ice sheet change during past glacial cycles is not only important for understanding past sea‐level change but also for predicting how ongoing glacial rebound contributes to future sea‐level change. Traditionally, a physics‐based "sea‐level model" is used to predict the sea‐level change associated with a particular reconstruction of past ice sheet change and compare the results with geological records of past sea level. However, a fundamental limitation of this approach is the need to compute sea‐level change for a large number of plausible ice histories, which is often prohibited by the computational resources required to repeatedly solve the complex physical equations. In this paper, we describe a machine‐learning‐based statistical model that can mimic the behavior of a physics‐based sea‐level model. This statistical model is computationally cheap and we demonstrate that it is able to accurately predict global sea‐level change for a suite of 150 "unseen" ice histories. Our statistical model predicts sea‐level change 100–1,000 times faster than a physics‐based model, making it an ideal tool for investigating and improving our understanding of global ice sheet change. Key Points: The first attempt to build a deep‐learning based Glacial isostatic adjustment (GIA) emulator that can accurately predict global sea‐level change based on a given ice modelThis emulator (GEORGIA) can predict global sea‐level change history within 0.5 s with minor emulation errorThis GIA emulator along with two illustrative applications are available for use by the wider sea‐level community [ABSTRACT FROM AUTHOR]

Recent work found that soldering CdTe quantum dots together with a molecular CdTe polymer yielded field-effect transistors with much greater electron mobility than quantum dots alone. We present a computational study of the CdTe polymer using the active-space variational two-electron reduced density matrix (2-RDM) method. While analogous complete active-space self-consistent field (CASSCF) methods scale exponentially with the number of active orbitals, the active-space variational 2-RDM method exhibits polynomial scaling. A CASSCF calculation using the (48o,64e) active space studied in this paper requires 1024 determinants and is therefore intractable, while the variational 2-RDM method in the same active space requires only 2.1 x 107 variables. Natural orbitals, natural-orbital occupations, charge gaps, and Mulliken charges are reported as a function of polymer length. The polymer, we find, is strongly correlated, despite possessing a simple sp³-hybridized bonding scheme. Calculations reveal the formation of a nearly saturated valence band as the polymer grows and a charge gap that decreases sharply with polymer length. [ABSTRACT FROM AUTHOR]

The results of mantle convection simulations are fully determined by the input parameters and boundary conditions used. These input parameters can be for initialisation, such as initial mantle temperature, or can be constant values, such as viscosity exponents. However, knowledge of Earth-like values for many input parameters are very poorly constrained, introducing large uncertainties into the simulation of mantle flow. Convection is highly non-linear, therefore linearised inversion methods cannot be used to recover past configurations over more than very short periods of time, which makes finding both initial and constant simulation input parameters very difficult. In this paper, we demonstrate a new method for making inferences about simulation input parameters from observations of the mantle temperature field after billions of years of convection. The method is fully probabilistic. We use prior sampling to construct probability density functions for convection simulation input parameters, which are represented using neural networks. Assuming smoothness, we need relatively few samples to make inferences, making this approach much more computationally tractable than other probabilistic inversion methods. As a proof of concept, we show that our method can invert the amplitude spectra of temperature fields from 2D convection simulations, to constrain yield stress, surface reference viscosity and the initial thickness of primordial material at the CMB, for our synthetic test cases. The best constrained parameter is yield stress. The reference viscosity and initial thickness of primordial material can also be inferred reasonably well after several billion years of convection. [ABSTRACT FROM AUTHOR]

Owing to the increasing availability of computational resources, in recent years the probabilistic solution of non-linear, geophysical inverse problems by means of sampling methods has become increasingly feasible. Nevertheless, we still face situations in which a Monte Carlo approach is not practical. This is particularly true in cases where the evaluation of the forward problem is computationally intensive or where inversions have to be carried out repeatedly or in a timely manner, as in natural hazards monitoring tasks such as earthquake early warning. Here, we present an alternative to Monte Carlo sampling, in which inferences are entirely based on a set of prior samples--that is, samples that have been obtained independent of a particular observed datum. This has the advantage that the computationally expensive sampling stage becomes separated from the inversion stage, and the set of prior samples--once obtained--can be reused for repeated evaluations of the inverse mapping without additional computational effort. This property is useful if the problem is such that repeated inversions of independent data have to be carried out. We formulate the inverse problem in a Bayesian framework and present a practical way to make posterior inferences based on a set of prior samples. We compare the prior sampling based approach to a Markov Chain Monte Carlo approach that samples from the posterior probability distribution. We show results for both a toy example, and a realistic seismological source parameter estimation problem. We find that the posterior uncertainty estimates obtained based on prior sampling can be considered conservative estimates of the uncertainties obtained by directly sampling from the posterior distribution. [ABSTRACT FROM AUTHOR]

Whenever a geophysical image is to be constructed, a variety of choices must be made. Some, such as those governing data selection and processing, or model parametrization, are somewhat arbitrary: there may be little reason to prefer one choice over another. Others, such as defining the theoretical framework within which the data are to be explained, may be more straightforward: typically, an 'exact' theory exists, but various approximations may need to be adopted in order to make the imaging problem computationally tractable. Differences between any two images of the same system can be explained in terms of differences between these choices. Understanding the impact of each particular decision is essential if images are to be interpreted properly—but little progress has been made towards a quantitative treatment of this effect. In this paper, we consider a general linearized inverse problem, applicable to a wide range of imaging situations. We write down an expression for the difference between two images produced using similar inversion strategies, but where different choices have been made. This provides a framework within which inversion algorithms may be analysed, and allows us to consider how image effects may arise. In this paper, we take a general view, and do not specialize our discussion to any specific imaging problem or setup (beyond the restrictions implied by the use of linearized inversion techniques). In particular, we look at the concept of 'hybrid inversion', in which highly accurate synthetic data (typically the result of an expensive numerical simulation) is combined with an inverse operator constructed based on theoretical approximations. It is generally supposed that this offers the benefits of using the more complete theory, without the full computational costs.We argue that the inverse operator is as important as the forward calculation in determining the accuracy of results. We illustrate this using a simple example, based on imaging the density structure of a vibrating string. [ABSTRACT FROM AUTHOR]

Access to interactive, simulated work environments through virtual reality (VR) has potential in teaching safety in design (SiD) to engineering students with reduced risk, cost and inconvenience. However, there is limited understanding of what students learn from immersive VR interventions, or the outcomes from specific learning activities. This study investigated engineering students' SiD learning in engineering design units at an Australian university. Informed by blended cognitivist-constructivist and situative perspectives, the units embedded static visualisation, dynamic visualisation through VR simulation, and group discussion, within an authentic hazard identification and analysis process. Observational data and student performances were analysed. Students' hazard comprehension differed by type of hazard and by learning activity. Students demonstrated understanding of SiD processes, terminology, and relevance of these to engineering design. We conclude that VR simulation is an effective learning tool, however the combination of learning elements was important to achieve learning outcomes. [ABSTRACT FROM AUTHOR]

Concerns raised by Okazaki & Ueda (2022) on the paper by Sambridge et al. (2022) are addressed. Two issues are discussed and some new numerical results presented. The first concerns whether the properties of the Wasserstein time-series misfit introduced in our earlier paper will translate to model space non-uniqueness in a seismic waveform inversion setting. It is argued that this is unlikely, given the special conditions, which must exist between all observed/predicted seismic waveform pairs for non-uniqueness to result. The second issue discussed is the efficacy of using the Sliced Wasserstein algorithm of Bonneel et al. (2015) as an alternate to the marginal Wasserstein algorithm, as proposed by Okazaki & Ueda (2022). It is argued that for optimization-based waveform fitting, the Sliced Wasserstein algorithm is a viable alternate provided care is taken to ensure that conditions arise which do invalidate analytical derivative expressions of the resulting Wasserstein misfit. In practice, this would likely mean recasting the 2D Optimal Transport problem posed in our earlier paper onto unstructured grids. [ABSTRACT FROM AUTHOR]

The determination of earthquake source parameters is an important task in seismology. For many applications, it is also valuable to understand the uncertainties associated with these determinations, and this is particularly true in the context of earthquake early warning (EEW) and hazard mitigation. In this paper, we develop a framework for probabilistic moment tensor point source inversions in near real time. Our methodology allows us to find an approximation to p(m|d), the conditional probability of source models (m) given observations (d). This is obtained by smoothly interpolating a set of random prior samples, using Mixture Density Networks (MDNs)—a class of neural networks which output the parameters of a Gaussian mixture model. By combining multiple networks as ‘committees’, we are able to obtain a significant improvement in performance over that of a single MDN. Once a committee has been constructed, new observations can be inverted within milliseconds on a standard desktop computer. The method is therefore well suited for use in situations such as EEW, where inversions must be performed routinely and rapidly for a fixed station geometry. To demonstrate the method, we invert regional static GPS displacement data for the 2010 MW 7.2 El Mayor Cucapah earthquake in Baja California to obtain estimates of magnitude, centroid location and depth and focal mechanism. We investigate the extent to which we can constrain moment tensor point sources with static displacement observations under realistic conditions. Our inversion results agree well with published point source solutions for this event, once the uncertainty bounds of each are taken into account. [ABSTRACT FROM AUTHOR]

How do body-wave traveltimes constrain the Earth's radial (1-D) seismic structure? Existing 1-D seismological models underpin 3-D seismic tomography and earthquake location algorithms. It is therefore crucial to assess the quality of such 1-D models, yet quantifying uncertainties in seismological models is challenging and thus often ignored. Ideally, quality assessment should be an integral part of the inverse method. Our aim in this study is twofold: (i) we show how to solve a general Bayesian non-linear inverse problem and quantify model uncertainties, and (ii) we investigate the constraint on spherically symmetric P-wave velocity (VP) structure provided by body-wave traveltimes from the EHB bulletin (phases Pn, P, PP and PKP). Our approach is based on artificial neural networks, which are very common in pattern recognition problems and can be used to approximate an arbitrary function. We use a Mixture Density Network to obtain 1-D marginal posterior probability density functions (pdfs), which provide a quantitative description of our knowledge on the individual Earth parameters. No linearization or model damping is required, which allows us to infer a model which is constrained purely by the data.We present 1-D marginal posterior pdfs for the 22 VP parameters and seven discontinuity depths in our model. P-wave velocities in the inner core, outer core and lower mantle are resolved well, with standard deviations of ∼0.2 to 1 per cent with respect to the mean of the posterior pdfs. The maximum likelihoods of VP are in general similar to the corresponding ak135 values, which lie within one or two standard deviations from the posterior means, thus providing an independent validation of ak135 in this part of the radial model. Conversely, the data contain little or no information on P-wave velocity in the D′′ layer, the upper mantle and the homogeneous crustal layers. Further, the data do not constrain the depth of the discontinuities in our model. Using additional phases available in the ISC bulletin, such as PcP, PKKP and the converted phases SP and ScP, may enhance the resolvability of these parameters. Finally, we show how the method can be extended to obtain a posterior pdf for a multidimensional model space. This enables us to investigate correlations between model parameters. [ABSTRACT FROM AUTHOR]

Abstract: The centroid-moment-tensor (CMT) algorithm provides a straightforward, rapid method for the determination of seismic source parameters from waveform data. As such, it has found widespread application, and catalogues of CMT solutions – particularly the catalogue maintained by the Global CMT Project – are routinely used by geoscientists. However, there have been few attempts to quantify the uncertainties associated with any given CMT determination: whilst catalogues typically quote a ‘standard error’ for each source parameter, these are generally accepted to significantly underestimate the true scale of uncertainty, as all systematic effects are ignored. This prevents users of source parameters from properly assessing possible impacts of this uncertainty upon their own analysis. The CMT algorithm determines the best-fitting source parameters within a particular modelling framework, but any deficiencies in this framework may lead to systematic errors. As a result, the minimum-misfit source may not be equivalent to the ‘true’ source. We suggest a pragmatic solution to uncertainty assessment, based on accepting that any ‘low-misfit’ source may be a plausible model for a given event. The definition of ‘low-misfit’ should be based upon an assessment of the scale of potential systematic effects. We set out how this can be used to estimate the range of values that each parameter might take, by considering the curvature of the misfit function as minimised by the CMT algorithm. This approach is computationally efficient, with cost similar to that of performing an additional iteration during CMT inversion for each source parameter to be considered. The source inversion process is sensitive to the various choices that must be made regarding dataset, earth model and inversion strategy, and for best results, uncertainty assessment should be performed using the same choices. Unfortunately, this information is rarely available when sources are obtained from catalogues. As already indicated by Valentine and Woodhouse (2010), researchers conducting comparisons between data and synthetic waveforms must ensure that their approach to forward-modelling is consistent with the source parameters used; in practice, this suggests that they should consider performing their own source inversions. However, it is possible to obtain rough estimates of uncertainty using only forward-modelling. [Copyright &y& Elsevier]

SUMMARY Displacement time-series recorded by Global Positioning System (GPS) receivers are a new type of near-field waveform observation of the seismic source. We have developed an inversion method which enables the recovery of an earthquake's mechanism and centroid coordinates from such data. Our approach is identical to that of the 'classical' Centroid-Moment Tensor (CMT) algorithm, except that we forward model the seismic wavefield using a method that is amenable to the efficient computation of synthetic GPS seismograms and their partial derivatives. We demonstrate the validity of our approach by calculating CMT solutions using 1 Hz GPS data for two recent earthquakes in Japan. These results are in good agreement with independently determined source models of these events. With wider availability of data, we envisage the CMT algorithm providing a tool for the systematic inversion of GPS waveforms, as is already the case for teleseismic data. Furthermore, this general inversion method could equally be applied to other near-field earthquake observations such as those made using accelerometers. [ABSTRACT FROM AUTHOR]

SUMMARY What makes a seismogram look like a seismogram? Seismic data sets generally contain waveforms sharing some set of visual characteristics and features-indeed, seismologists routinely exploit this when performing quality control 'by hand'. Understanding and harnessing these characteristics offers the prospect of a deeper understanding of seismic waveforms, and opens up many potential new techniques for processing and working with data. In addition, the fact that certain features are shared between waveforms suggests that it may be possible to transform the data away from the time domain, and represent the same information using fewer parameters. If so, this would be a significant step towards making fully non-linear tomographic inversions computationally tractable. Hinton & Salakhutdinov showed that a particular class of neural network, termed 'autoencoder networks', may be used to find lower-dimensional encodings of complex binary data sets. Here, we adapt their work to the continuous case to allow the use of autoencoders for seismic waveforms, and offer a demonstration in which we compress 512-point waveforms to 32-element encodings. We also demonstrate that the mapping from data to encoding space, and its inverse, are well behaved, as required for many applications. Finally, we sketch a number of potential applications of the technique, which we hope will be of practical interest across all seismological disciplines, and beyond. [ABSTRACT FROM AUTHOR]

The ability to handle large amounts of data automatically is essential for any major tomographic inversion. As part of this process, it is necessary to differentiate between high-quality seismograms, and those that are unusable due to noise or other errors. This quality assessment is traditionally made visually; however, the sheer quantity of data in a modern tomographic data set makes this approach unfeasible. It is therefore necessary to develop techniques for automating this quality assessment process. We demonstrate that a simple neural network, trained to recognize the frequency-domain characteristics of high- and low-quality data, can successfully distinguish the two classes in unseen data. We demonstrate that the resulting clean data sets are of sufficient quality to allow full-waveform determination of event focal mechanisms and hypocentral parameters. The process we outline allows the rapid creation of a high-quality data set for seismic tomography. Depending on application, this may be suitable for use without further refinement. In some circumstances, a further visual inspection may remain desirable to ensure the data set is noise-free; however, a significant benefit will still derive from the reduction in number of traces to be examined. This will enable full-waveform inversion using significantly larger data sets than has hitherto been possible. The selection strategy relies only on measurements made from the seismogram, and on rough estimates of hypocentral location—the final data set does not depend on any a priori assumptions regarding earth structure or wave propagation. Our focus has been on data selection for seismic tomography, but the approach is general and may find application across a wide range of seismic investigations. An automated system is of interest wherever large data sets must be handled, or where time is of the essence—such as in earthquake hazard assessment. [ABSTRACT FROM AUTHOR]

To perform seismic tomography, accurate determinations of event locations and focal mechanisms are required. These are usually obtained prior to tomographic inversion; often, little attention is paid to the earth model used for their determination. We show that an imprint of this model is found in the model recovered after tomographic inversion. To reduce this problem, it is important to recalculate earthquake source parameters as the inversion proceeds; synthetic tests suggest that this yields a better correspondence between recovered and true models. However, alternate source and structure inversions lead to a slow rate of convergence, and significant errors remain. We therefore propose combining source and structure inversion into a single inverse problem, and present an efficient algorithm to do this. We demonstrate that this reduces the number of iterations required to achieve a given accuracy; in our experiments, we observe a fourfold improvement on alternate inversions. We focus on full-waveform inversions for both source and structure, although the methods presented are general and should be applicable to other techniques. [ABSTRACT FROM AUTHOR]

We propose a new approach to measuring the agreement between two oscillatory time-series, such as seismic waveforms, and demonstrate that it can be used effectively in inverse problems. Our approach is based on Optimal Transport theory and the Wasserstein distance, with a novel transformation of the time-series to ensure that necessary normalization and positivity conditions are met. Our measure is differentiable, and can readily be used within an optimization framework. We demonstrate performance with a variety of synthetic examples, including seismic source inversion, and observe substantially better convergence properties than achieved with conventional L 2 misfits. We also briefly discuss the relationship between Optimal Transport and Bayesian inference. [ABSTRACT FROM AUTHOR]

High‐energy X‐ray scattering and pair distribution function analysis (HEXS/PDF) is a powerful method to reveal the structure of materials lacking long‐range order, but is underutilized for molecular complexes in solution. We demonstrate the application of HEXS/PDF with 0.26 Å resolution to uncover the solution structure of five bimetallic CuI/RuII/OsII complexes. HEXS/PDF of each complex in acetonitrile solution confirms the pairwise distances in the local coordination sphere of each metal center as well as the metal⋅⋅⋅metal distances separated by over 12 Å. The metal⋅⋅⋅metal distance detected in solution is compared with that from the crystal structure and molecular models to confirm that distortions to the metal bridging ligand are unique to the solid state. This work presents the first example of observing sub‐Ångström conformational differences by direct comparison of solution phase and solid‐state structures and shows the potential for HEXS/PDF in the determination of solution structure of single molecules. [ABSTRACT FROM AUTHOR]

High‐energy X‐ray scattering and pair distribution function analysis (HEXS/PDF) is a powerful method to reveal the structure of materials lacking long‐range order, but is underutilized for molecular complexes in solution. We demonstrate the application of HEXS/PDF with 0.26 Å resolution to uncover the solution structure of five bimetallic CuI/RuII/OsII complexes. HEXS/PDF of each complex in acetonitrile solution confirms the pairwise distances in the local coordination sphere of each metal center as well as the metal⋅⋅⋅metal distances separated by over 12 Å. The metal⋅⋅⋅metal distance detected in solution is compared with that from the crystal structure and molecular models to confirm that distortions to the metal bridging ligand are unique to the solid state. This work presents the first example of observing sub‐Ångström conformational differences by direct comparison of solution phase and solid‐state structures and shows the potential for HEXS/PDF in the determination of solution structure of single molecules. [ABSTRACT FROM AUTHOR]

The reaction between a two-coordinate Co(II) diaryl complex and "GaI" affords 2,6-Pmp2C6H3CoGa3I5, in a new geometry for a heavier group 13-transition metal cluster. Experimental and computational investigations show that this compound is best described as a nido metalla-group 13 cluster. [ABSTRACT FROM AUTHOR]

We report a study of chromophore-catalyst assemblies composed of light harvesting hexabenzocoronene (HBC) chromophores axially coordinated to two cobaloxime complexes. The chromophore-catalyst assemblies were prepared using bottom-up synthetic methodology and characterized using solid-state NMR, IR, and x-ray absorption spectroscopy. Detailed steady-state and time-resolved laser spectroscopy was utilized to identify the photophysical properties of the assemblies, coupled with time-dependent DFT calculations to characterize the relevant excited states. The HBC chromophores tend to assemble into aggregates that exhibit high exciton diffusion length (D = 18.5 molecule2/ps), indicating that over 50 chromophores can be sampled within their excited state lifetime. We find that the axial coordination of cobaloximes leads to a significant reduction in the excited state lifetime of the HBC moiety, and this finding was discussed in terms of possible electron and energy transfer pathways. By comparing the experimental quenching rate constant (1.0 × 109 s−1) with the rate constant estimates for Marcus electron transfer (5.7 × 108 s−1) and Förster/Dexter energy transfers (8.1 × 106 s−1 and 1.0 × 1010 s−1), we conclude that both Dexter energy and Marcus electron transfer process are possible deactivation pathways in CoQD-A. No charge transfer or energy transfer intermediate was detected in transient absorption spectroscopy, indicating fast, subpicosecond return to the ground state. These results provide important insights into the factors that control the photophysical properties of photocatalytic chromophore-catalyst assemblies. [ABSTRACT FROM AUTHOR]

We apply Hamilton's principle to obtain the exact equations of motion for an elastic planet that is rotating, self-gravitating and comprises both fluid and solid regions. This variational problem is complicated by the occurrence of tangential slip at fluid--solid boundaries, but we show how this can be accommodated both directly and using the method of Lagrange multipliers. A novelty of our approach is that the planet's motion is described relative to an arbitrary reference configuration, with this generality offering advantages for numerical calculations. In particular, aspherical topography on the free surface or internal boundaries of the planet's equilibrium configuration can be converted exactly into effective volumetric heterogeneities within a geometrically spherical reference body by applying a suitable particle relabelling transformation. The theory is then specialized to consider the linearized motion of a planet about a steadily rotating equilibrium configuration, with these results having applications to normal mode coupling calculations used within studies of long-period seismology, tidal deformation and related fields. In particular, we explain how our new theory will, for the first time, allow aspherical boundary topography to be incorporated exactly within such coupling calculations. [ABSTRACT FROM AUTHOR]

Strongelectron correlation within a quasi-spin model of chromophoreswas recently shown to enhance exciton energy transfer significantly.Here we investigate how the modulation of the electronic structureof the chromophores by chemical substitution can enhance energy-transferefficiency. Unlike previous work that does not consider the directeffect of the electronic structure on exciton dynamics, we add chemicalsubstituents to the fluorone dimer to study the effect of electron-donatingand electron-withdrawing substituents on exciton energy transfer.The exciton dynamics are studied from the solution of a quantum Liouvilleequation for an open system whose model Hamiltonian is derived fromexcited-state electronic structure calculations. Both van der Waalsenergies and coupling energies, arising from the Hellmann–Feynmanforce generated upon transferring the dimers from infinity to a finiteseparation, are built into the model Hamiltonian. Though these twoeffects are implicitly treated in dipole-based models, their explicitand separate treatment as discussed here is critical to forging thecorrect connection with the electronic structure calculations. Wefind that the addition of electron-donating substituents to the fluoronesystem results in an increase in exciton-transfer rates by factorsranging from 1.3–1.9. The computed oscillator strength is consistentwith the recent experimental results on a larger heterodimer systemcontaining fluorone. The oscillator strength increases with the additionof electron-donating substituents. Our results indicate that the studyof chromophore networks via electronic structure will help in thefuture design of efficient synthetic light-harvesting systems. [ABSTRACT FROM AUTHOR]

Metal-metal interactions, pair distribution function analysis, structure elucidation, X-ray diffraction, X-ray scattering Keywords: metal-metal interactions; pair distribution function analysis; structure elucidation; X-ray diffraction; X-ray scattering EN metal-metal interactions pair distribution function analysis structure elucidation X-ray diffraction X-ray scattering 1 1 1 01/20/22 20220126 NES 220126 B The solution-phase molecular structure b of five redox- and photo-active bimetallic complexes was directly probed using high-energy X-ray scattering and atomic pair distribution function analysis in the Research Article by Xiaosong Li, Karen L. Mulfort et al. (e202111764). Cover Picture: Bimetallic Copper/Ruthenium/Osmium Complexes: Observation of Conformational Differences Between the Solution Phase and Solid State by Atomic Pair Distribution Function Analysis (Angew. [Extracted from the article]

Metal-metal interactions, pair distribution function analysis, structure elucidation, X-ray diffraction, X-ray scattering Titelbild: Bimetallic Copper/Ruthenium/Osmium Complexes: Observation of Conformational Differences Between the Solution Phase and Solid State by Atomic Pair Distribution Function Analysis (Angew. Keywords: metal-metal interactions; pair distribution function analysis; structure elucidation; X-ray diffraction; X-ray scattering EN metal-metal interactions pair distribution function analysis structure elucidation X-ray diffraction X-ray scattering 1 1 1 01/20/22 20220126 NES 220126 B Die Molekülstrukturen b von fünf redox- und photoaktiven Bimetallkomplexen in der Lösungsphase wurden im Forschungsartikel von Xiaosong Li, Karen L. Mulfort und Mitarbeitern mittels Hochenergie-Röntgenstreuung und paarweiser Atomverteilungsfunktion aufgedeckt (e202111764). [Extracted from the article]

• Equation of state (EOS) modelling is a powerful tool to estimate mineral properties. • Conventional explicit EOSs which are assumed to follow certain form provide a biased estimate of uncertainties. • Experimental errors, data inconsistency and sparsity all contribute to uncertainties in mineral seismic properties. • Neural networks based approach captures full uncertainties, highlights data gaps and identifies data inconsistencies. • The approach should improve uncertainty quantification in interpretations of seismic data. Interpretation of information available from seismic data in terms of temperature and composition requires an understanding of the physical properties of minerals, in particular, the elastic properties of candidate Earth minerals at the relevant (here, lower mantle) pressure and temperature. A common practice for the bulk elastic properties is to measure volume at a range of pressures and temperatures using experiments or computational methods. These datasets are then typically fit to a pre-determined functional form, or equation of state to allow computation of elastic properties at any other pressure or temperature. However, errors, both random and systematic, limitations in the number of data and choice of pressure marker and scale, as well as different functional forms of equations of state, all contribute to the uncertainties in mineral seismic properties. In an attempt to present a more comprehensive view of these uncertainties, we use neural-network based techniques to infer the relationship among: pressure, temperature, volume, bulk modulus, and thermal expansivity of MgO. We illustrate our approach on experimental data, but an extension to ab initio data is straightforward. The type of neural network used is called a Mixture Density Network (MDN) which is a combination of a conventional feed-forward neural network and a mixture model that consists of Gaussian functions. MDNs are capable of approximating arbitrary probability density functions, which allows us to compute the uncertainties in the predicted equations of state. Since the networks interpolate locally between input samples, pressure-volume-temperature relations are implicitly learned from data without imposing any explicit thermodynamic assumptions or ad-hoc relationships. We use the partial derivatives of the mapping between inputs (pressure and temperature) and output (volume) to compute the isothermal bulk modulus and thermal expansivity. Flexibility of the MDNs allows us to investigate the uncertainty due to certain data in one region of pressure-temperature space without influencing the posterior probability density everywhere. In general, we find that the elastic properties of MgO are well-constrained by experimental data. However, our study highlights regions in which sparse or inconsistent data lead to poorly constrained elastic properties, namely: at low pressure and high temperature (<25 GPa and >1500 K), and temperatures above 2700 K. While the former conditions are likely not important for the Earth's lower mantle, they are relevant in other planetary bodies such as the Moon and Mars. Comparison with conventional equation of state forms shows that assuming a certain functional form of the pressure-volume-temperature relationship leads to potential bias in uncertainty quantification, because the uncertainties are then specific to the underlying form. In combination with data sets of other lower mantle minerals, this technique should improve uncertainty quantification in interpretations of seismic data. [ABSTRACT FROM AUTHOR]